Bayesian spatio-temporal modelling of national milk-recording data of seasonal-calving New Zealand dairy herds

Clements, A. C. A., Pfeiffer, D. U. and Hayes, D. (2005). Bayesian spatio-temporal modelling of national milk-recording data of seasonal-calving New Zealand dairy herds. In: Peter A. Durr and S. Wayne Martin, GISVET'04. 2nd International Conference on the Applications of GIS and Spatial Analysis to Veterinary Science, Ontario, Canada, (183-196). 23-25 June 2004. doi:10.1016/j.prevetmed.2005.07.004


Author Clements, A. C. A.
Pfeiffer, D. U.
Hayes, D.
Title of paper Bayesian spatio-temporal modelling of national milk-recording data of seasonal-calving New Zealand dairy herds
Conference name 2nd International Conference on the Applications of GIS and Spatial Analysis to Veterinary Science
Conference location Ontario, Canada
Conference dates 23-25 June 2004
Proceedings title GISVET'04   Check publisher's open access policy
Journal name Preventive Veterinary Medicine   Check publisher's open access policy
Place of Publication The Netherlands
Publisher Elsevier BV
Publication Year 2005
Sub-type Fully published paper
DOI 10.1016/j.prevetmed.2005.07.004
ISBN 189951323X
9781899513239
ISSN 0167-5877
Editor Peter A. Durr
S. Wayne Martin
Volume 71
Issue 3-4
Start page 183
End page 196
Total pages 14
Language eng
Abstract/Summary A spatio-temporal analysis was undertaken with the aim of identifying the dynamics of herd mean individual cow SCCs (MICSCC) in seasonally calving New Zealand dairy herds. Two datasets were extracted from the Livestock Improvement Corporation's extensive national dairy recording database: (1) milk-recording data aggregated at the herd-level and (2) sales questionnaire data containing information on the size, location and infrastructure of each farm. A Bayesian spatio-temporal modelling approach was applied to the analysis. The data were aggregated by 10 km(2) grid cells and linear regression models were developed with spatially structured and unstructured random effects, a linear temporal trend random effect and spatial-temporal interactions for log-transformed median MISCC (ln(median MISCC)). Significant associations were found between ln(median MISCC) and milk yield, milk fat, milk protein, farm area and number of cups in the dairy. This led us to suggest that SCCs should be adjusted for volume and constituents prior to determining a threshold MISCC for identification of subclinical mastitis (SCM) problem herds. Part, or all, of the temporal trend in MISCC in the spatio-temporal model was accounted for by inclusion of yield and milk constituents as independent variables. This supports the hypothesis of a dilution effect with potential consequences for misdiagnosis of SCM, particularly in late lactation. Unmeasured covariates were similarly likely to be spatially structured and unstructured. (c) 2005 Elsevier B.V. All rights reserved.
Keyword Veterinary Sciences
Subclinical mastitis
Somatic cell counts
Milk yield
Milk fat
Milk-recording
Spatio-temporal modelling
Bayesian modelling
Somatic-cell Counts
Bulk Tank Milk
Clinical Mastitis
Management-practices
Risk-factors
Subclinical Mastitis
Udder Health
Cows
Association
Lactation
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

 
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Created: Wed, 17 Oct 2007, 14:07:26 EST